Why Data Science is Important For Your Business


Table Of Contents

Using consumer data and the data from internal operations, companies can increase sales, reduce churn, improve customer retention, improve organizational efficiency, and much more.   However, organizations cannot use the generated data directly to fulfill these goals. Instead,  they need to process the raw data to create insights. This is where data science comes into play. This article discusses some use cases to explain why is data science important. It also discusses what data science platforms are, some providers, their advantages, and their disadvantages.

What is a Data Science Platform?

A data science platform can be defined as providing solutions for end-to-end data science tasks in one place. Each data science platform should be able to provide solutions for one or more tasks in the life cycle of a data science project.

The solutions may include services for one or more events from the following tasks:

  • Data ingestion
  • Data cleaning
  • Data transformation
  • Data modeling
  • Data visualization
  • Development and deployment of machine learning models
  • Collaboration and sharing

Why is Data Science Important?

Data science is to data what cooking skills are to a raw food item. Data has no intrinsic value without data science. It will eventually become a liability for a business to store and manage data that has no business value.

Data science enables companies to gather information and generate actionable insights from the gathered data. Using these insights, companies can make more informed business decisions.

To understand why data science is important, let us look at some of the business aspects where data science has the potential to create an impact.

What Does Data Science Do?

Data science enables a business to work efficiently and accurately in many aspects.

  1. Data science allows management and the stakeholders in a company to make better decisions. By providing insights from available data, data science can help identify the behavior of consumers, the supply chain bottlenecks, reasons for product defects, and many more. Using this information, the management team can make better decisions to tackle any given problem.
  2. Data science helps companies identify new opportunities. By analyzing data, companies can gather information such as sales trends, expenditure on raw materials, inventory costs, and more. Using this information, companies can decide to enter into new markets, resize their inventory, increase or decrease their output in particular regions, and make other essential business decisions.
  3. Data science helps companies in providing personalized customer service. Most of the B2C companies like Amazon, eBay, Netflix, YouTube, and Spotify, use consumer behavior to create personalized services. E-commerce companies analyze an individual user’s buying behavior to suggest new products. Personalization helps companies increase the time spent by users on their apps. This also results in a good consumer experience and often translates into more revenue for the company.
  4. Data science helps companies increase their profits. Companies like Uber, DoorDash, Lyft, etc., in cab or delivery services often use data science to increase their profits. For instance, when demand is high, Uber increases prices accordingly. Similarly, machine learning models also dynamically decide how a cab or delivery partner will be assigned a task. All these activities are performed using the live data of people trying to use theseservices and the number of service providers in the area. Without data science, it would be almost impossible to facilitate these tasks at the speed and level of accuracy needed for maximum efficency.
  5. Data science saves lives. With the advent of advanced healthcare technologies like MRI, ECG, and gadgets like smartwatches, healthcare data is readily available to companies in digital form.  Smartwatches can easily measure stress levels, pulse rate, SpO2, and other vital metrics. Companies can analyze the data to detect patterns in the data and raise warnings if they detect any abnormalities. For example, A man in India was saved after his Apple smartwatch notified him of fluctuations in his heart rate.
  6. Data science helps governments fight diseases. During the Covid-19 pandemic, several countries used data science to handle the situation.  Governments used data science to monitor and track the spread of diseases. The government used the covid-19 data in India to decide on containment zones. The Defence Research and Development Organization (DRDO), Government of India, developed an artificial intelligence-based mobile application to detect Covid using chest x-Rays and CT-Scans.

Features of Data Science Platforms


Companies like Revelate, Databricks, and Snowflake use the PaaS model to provide a data science platform as a service. These data science platforms are hosted on the individual company’s servers, and you can access the platform as cloud data science software.

Although the features and services provided by different data science platforms might differ, they should have a subset of the following data science platform features:

  1. An enterprise data science platform facilitates a subset of tasks from data ingestion, data preprocessing, data modeling, and data visualization. It also facilitates developing and deploying machine learning models for predictive analytics.
  2. Data science platforms allow collaboration and sharing of data, resources, and insights. They should perform as a platform as a service for data science enabling the data scientists to log in and work from anywhere in the world. With increased collaboration, the efficiency of the data science team also increases.
  3. A cloud data science platform helps companies collect data from various sources into a single cloud space without having privacy concerns. Data science platforms also ensure data availability by backing up the data into various servers stored at different geographical locations. This averts the risk of data loss due to natural disasters or occupation hazards.
  4. Data science platforms provide tools to analyze the inputs and outputs of the processes in the entire project life cycle. This helps the teams analyze different metrics of data as well as the machine learning models to evaluate the accuracy and efficiency of the tasks being executed.
  5. A cloud-based data science platform helps data scientists focus on finding solutions to business problems instead of configuration and management of resources. Data science platforms provide web-based APIs for all tasks. With auto-scaling, enterprise data science platforms enable data scientists to work on any size of data without worrying about computational power or storage capacity.
  6. Data science platforms allow the integration of different tools and technologies. Adopting a new technology or tool isn’t always easy. Therefore, enterprise data science platforms allow you to work on the tool of your choice. You can simply integrate your tool into the platform and start working. These platforms also allow you to work with different tools and technologies in a single data science project. For instance, you can use pandas for data preprocessing and PySpark for feature calculation and model building. You can also integrate software modules written in different languages. Therefore, these platforms allow you to work easily by integrating your current development environment into the cloud data science platform.

Use Cases of a Data Science Platform as a Service

From predicting earthquakes to classifying loan applications, data science has its use in each task. This section discusses the uses of data science in different industries.

Data Science Platform for Healthcare

Data science is almost indispensable to healthcare. The availability of digital patient data, genome sequencing, and healthcare expert systems have paved the way for many data science use cases in healthcare.

  • Using patient data at personal and community levels, companies and governments can leverage predictive analytics in healthcare. They can also use historical data and real-time vitals to predict a person’s risk level for certain diseases.
  • Governments can use data science to predict the spread of a disease as it was done in the Covid-19 pandemic. Similarly, historical data can be used to identify the causes of a disease in a particular community.
  • Data science allows doctors to analyze medical images such as MRI and X-Ray images to a different level. Data science tools for patient imaging can be created using the medical images from diagnosis of other patients matching a particular diagnosis. These tools are then able to accurately predict diseases based on the MRI scans and X-Ray images of a new person. For example, during the Covid-19 pandemic, RT-PCR tests failed to detect the covid-19 virus due to mutations. In such a situation, many data science tools were created that are able to correctly identify if a person has covid-19 or not just by using MRI and X-Ray images. In an another instance, a data science tool was created to identify COVID infection using the voice of the patient. 
  • Private healthcare organizations can also use data science platforms to improve service quality. They can use expert systems to expedite the process of diagnosis. These expert systems are softwares created using the expertise of doctors and the historical healthcare data. Based on the patient’s test results, an expert system can easily identify diseases and provide prescriptions for them in seconds. Doctors can verify the results and quickly start treatment.
  • Drug discovery is one of the most crucial use cases of data science in healthcare. Genome sequencing-driven drug discovery has helped governments save millions of lives by expediting the process of drug discovery, especially vaccines. Now, scientists can identify genomic patterns of a virulent disease and create an m-RNA or DNA vaccine with desired proteins that help the human body create immunity against a viral disease.

Data Science Platform for Manufacturing

Manufacturing companies can also use data science platforms to improve their workflow and increase productivity. Here are some of the use cases of data science platforms in manufacturing industries.

  • Demand forecasting: Manufacturing industries can use data science platforms to predict product demand according to macroeconomic and microeconomic conditions. This can help them maintain their inventory to avoid shortage or excess of goods.
  • Supply chain management: The supply chain of any manufacturing industry is very complex. Many bottlenecks and failure points in the entire supply chain can negatively affect the business. Companies can take advantage of data science platforms and the data generated in the production process to identify the bottlenecks and predict the possibilities of production delays. They can also avert big project failures by analyzing and optimizing the production schedule.
  • Price Optimization: The price of any product depends on various factors such as cost of production, competitors, purchasing power of the consumers, etc. Companies can use data science to analyze all the aspects to decide on the price of a product that will maximize sales and profits.

Data Science Platforms for Insurance

Insurance companies can also use enterprise data science platforms to leverage historical insurance data to maximize profits. Following are some of the use cases of data science for the insurance industry.

  • Risk assessment: Companies Insurance companies use data science to identify the claim patterns of their existing customers. This helps them determine the potential risk for any new client, which helps decide policy premiums.
  • Policy recommendation: Insurance companies can use data science to predict client lifetime value based on the client’s income, age, occupation, and more. Accordingly, the platform can recommend policies to the client using personalization. This will maximize the chance of the client buying a policy and hence profits will increase for the company.
  • Fraud detection: Insurance frauds can cause big losses to companies. Insurance companies can correctly identify the likelihood of a claim being fraudulent or not using classification and prediction tools. This helps them avert losses due to suspicious activities and fraud claims.

Leading Data Science Platform Providers

There are many cloud data science platforms in the data science platform market. Here, we will discuss three leading cloud data science platforms in the world, their advantages, and their disadvantages.


Snowflake is a cloud data science platform officially launched in 2014. It provides flexible and easy-to-use tools for storing, processing, and analysis of data.

Advantages of Snowflake Disadvantages of Snowflake
Snowflake provides a highly secure environment for data storage and processing. Snowflake is a data science platform as a service tool. Hence, it cannot be deployed on on-premises infrastructure. You might need to move all your data to a public cloud infrastructure to use snowflake.
It provides great performance and scalability. Snowflake is a closed, proprietary system where data sharing outside of the system isn’t possible. This narrows potential customer bases and access.
Snowflake stores data in micro partitions of 50 to 500 Megabytes. This helps in faster retrieval of data. Snowflake uses an on-demand pricing strategy. Hence, you pay for only the storage and CPU time that you use. Although it is a good thing, it can become costlier if the data size or processing requirements become large.
It has a flat learning curve. Thus it is very easy for data scientists to start with snowflakes and work efficiently. It also provides great documentation to learn from. Snowflake has a very new and relatively small community. Hence, if the data scientists get into an error while working, they might need to figure it out on their own.
Snowflake provides many integration tools. It is accessed using its web application, command line interface, and connectors of different programming languages. Snowflake can be integrated with almost every cloud data warehouse. However, using it with other data warehouses can be a disadvantage because most cloud data warehouse providers such as Amazon AWS, Microsoft Azure, and Google Cloud also provide processing and analytical tools. Using all the services from a single vendor can be more beneficial instead of using snowflake.


Databricks provides different cloud-based tools for data warehousing, data engineering, streaming, data science, and machine learning. It delivers reliability, good performance, and strong data governance by combining the best elements of data lakes and data warehouses along with machine learning.

Databricks’ most recent offering, the Lakehouse platform provides all the features of data warehouses as well as an analytics tool. If you know SQL along with Python, Scala, or R programming language, you can easily process and analyze your data using Databricks.

Advantages of Databricks Disadvantages of Databricks
Data is designed to be scalable. It allows organizations to process big data and perform complex data analytics tasks very easily. Companies can easily upscale or downscale their resources according to their data size and processing requirements. If you don’t know how to code, working on the Databricks platform will be very difficult. It is ideal for data scientists who work with Python or R programming languages.
Databricks can be easily integrated with popular cloud data warehouses such as Google Cloud, Amazon AWS, and Microsoft Azure. This allows the companies to analyze large amounts of data without worrying about transferring them to Databricks. Databricks can be complex to use for organizations that are new to big data processing. It is not suited for business analysts or data analysts with no programming knowledge.
It supports all the machine learning and deep learning frameworks. To build machine learning models, Databricks supports AutoML and hyperparameter optimization. Even if AutoML is available, you need strong data science and machine learning skills to work with Databricks products.
Databricks supports many tools for collaboration. As it’s a cloud data science platform, data scientists can easily access it from their own systems anywhere in the world. Databricks also use an on-demand pricing strategy. Hence, using Databricks can be costly for organizations with large data processing needs.
Overall, Databricks provides a unified solution for big data processing and data science tasks. So, it can help organizations reduce costs compared to using traditional data warehouses and data science solutions. Although Databricks provides many tools for processing big data, it might not be possible to customize the tools according to an organization’s specific requirements.


Revelate is a data science platform that facilitates data monetization and fulfillment. It helps companies share and commercialize their data in a secure environment by handling all the security and licensing issues. With Revelate, you can seamlessly move data from one platform to another without worrying about security issues.

Revelate provides access to internal and external data that can help organizations create innovative new products and service ideas. It can also help them develop and fine-tune existing products and services to serve their customers and other stakeholders better.

Advantages of Revelate Disadvantages of Revelate
Revelate helps data companies maintain data fidelity by facilitating real-time access to data. Revelate doesn’t provide analytical tools for data processing and analysis.
Revelate handles complex data licensing tasks. Companies can control privacy and access levels for business-sensitive data stored at platforms like Databricks. Revelate is a data fulfillment platform. You cannot build machine learning models for predictive analytics. For this, you integrate it with platforms like Databricks.
Revelate helps companies share data products at scale. It encourages collaboration between customers and data companies by providing the right data products in the right configuration. Revelate doesn’t provide a unified solution for big data processing and data science tasks.
Using Revelate, companies can monetize their data lying in the data warehouses for monetary benefits. It facilitates the entire process of transferring data from one party to another.
Revelate is platform agnostic, which means that data can be moved from any source to any target (provided that the target supports the data format)
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Which Data Science Platform Should You Choose for Your Business?

Companies should choose an enterprise data science platform according to their needs. If you are looking to monetize or transfer your data, Revelate can be a great platform for you. It will handle all your needs regarding data monetization and data sharing. If you want to build a data product and then monetize it, you can use data science platforms like Snowflake and Databricks along with Revelate to fulfill your tasks.

  1. If your company is already using Databricks, Revelate provides you with a fully configurable environment to monetize your data. You can create data products without even worrying about data transfer. Revelate also provides a data marketplace to sell the data products created by the companies.
  2. Revelate handles the complex processes of licensing and transferring data. It also ensures that the companies have all the necessary privileges to perform secure data transfers in an open environment.
  3. If you want to use existing data stored at your servers to analyze them and build predictive models using Databricks or Snowflake, Revelate can help you transfer data from company servers to Databricks or Snowflake without worrying about data security.
  4. Platforms such as Snowflake are closed, proprietary systems where data sharing and sales outside of the system aren’t possible. This narrows potential customer bases and access. Revelate allows companies to extract data from Snowflake, refine it, and prepare it into data products that can be shared or sold to anyone.



In this article, we have discussed the basics of data science, why is data science important, and how different data science platforms can be used to perform different tasks. We also discussed the use of data science platforms in different industries such as healthcare, manufacturing, and insurance.

Start harnessing the power of data today. Learn how a data science platform can benefit your organization and take the first step toward data-driven growth and success. Start your free trial today.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started